Penerapan Deep Learning Menggunakan VGG-16 untuk Klasifikasi Citra Glioma

 (*)Annisa Putri Mail (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Benny Sukma Negara (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Suwanto Sanjaya (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)

(*) Corresponding Author

Submitted: May 13, 2022; Published: June 30, 2022


One of the types of brain tumors in humans is glioma. Glioma is considered to be the most common type of primary brain tumor in adults. To determine the follow-up action that will be carried out by the doctor, the level of glioma needs to be known first. Glioma is divided into 3 grades. To be able to distinguish grades from gliomas, a classification process can be carried out using deep learning with CNN architecture. Glioma grade classification applies Histogram Equalization (HE) preprocessing. The training model uses CNN with the VGG-16 architecture. data using split data with a comparison of 70% training 30% testing, 80% training 20% testing, and 90% training 10% testing. The results of this study using original data have better results compared to data using HE preprocessing on batch size 16 testing and split data 90% training 10% testing.


Glioma; Deep Learning; CNN; HE; VGG-16

Full Text:


Article Metrics

Abstract view : 327 times
PDF - 528 times


P. Sun, D. Wang, V. C. Mok, and L. Shi, “Comparison of Feature Selection Methods and Machine Learning Classifiers for Radiomics Analysis in Glioma Grading,” IEEE Access, vol. 7, pp. 102010–102020, 2019, doi: 10.1109/ACCESS.2019.2928975.

P. Wesseling and D. Capper, “WHO 2016 Classification of gliomas,” Neuropathol. Appl. Neurobiol., vol. 44, no. 2, pp. 139–150, 2018, doi: 10.1111/nan.12432.

S. Kumar, A. Negi, J. N. Singh, and A. Gaurav, “Brain Tumor Segmentation and Classification Using MRI Images via Fully Convolution Neural Networks,” Proc. - IEEE 2018 Int. Conf. Adv. Comput. Commun. Control Networking, ICACCCN 2018, pp. 1178–1181, 2018, doi: 10.1109/ICACCCN.2018.8748614.

H. H. Sultan, N. M. Salem, and W. Al-Atabany, “Multi-Classification of Brain Tumor Images Using Deep Neural Network,” IEEE Access, vol. 7, pp. 69215–69225, 2019, doi: 10.1109/ACCESS.2019.2919122.

Z. Lu et al., “The classification of gliomas based on a Pyramid dilated convolution resnet model,” Pattern Recognit. Lett., vol. 133, pp. 173–179, 2020, doi: 10.1016/j.patrec.2020.03.007.

S. Lahmiri, “Glioma detection based on multi-fractal features of segmented brain MRI by particle swarm optimization techniques,” Biomed. Signal Process. Control, vol. 31, pp. 148–155, 2017, doi: 10.1016/j.bspc.2016.07.008.

A. Kabir Anaraki, M. Ayati, and F. Kazemi, “Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms,” Biocybern. Biomed. Eng., vol. 39, no. 1, pp. 63–74, 2019, doi: 10.1016/j.bbe.2018.10.004.

W. Souma, I. Vodenska, and H. Aoyama, “Enhanced news sentiment analysis using deep learning methods,” J. Comput. Soc. Sci., vol. 2, no. 1, pp. 33–46, 2019, doi: 10.1007/s42001-019-00035-x.

S. Banerjee, B. R. Ghosh, A. Gangapadhyay, and H. S. Chatterjee, “Galaxy morphological image classification using resnet,” Iraqi J. Sci., vol. 62, no. 10, pp. 3690–3696, 2021, doi: 10.24996/ijs.2021.62.10.27.

J. Hu, X. Zhang, and S. Maybank, “Abnormal Driving Detection with Normalized Driving Behavior Data: A Deep Learning Approach,” IEEE Trans. Veh. Technol., vol. 69, no. 7, pp. 6943–6951, 2020, doi: 10.1109/TVT.2020.2993247.

W. Yang, L. Zhou, T. Li, and H. Wang, “A Face Detection Method Based on Cascade Convolutional Neural Network,” Multimed. Tools Appl., vol. 78, no. 17, pp. 24373–24390, 2019, doi: 10.1007/s11042-018-6995-0.

M. Havaei et al., “Brain tumor segmentation with Deep Neural Networks,” Med. Image Anal., vol. 35, pp. 18–31, 2017, doi: 10.1016/

N. Veni and J. Manjula, “High-performance visual geometric group deep learning architectures for MRI brain tumor classification,” J. Supercomput., Mar. 2022, doi: 10.1007/S11227-022-04384-9.

O. N. Belaid and M. Loudini, “Classification of Brain Tumor by Combination of Pre-Trained,” 2020, doi: 10.22059/jitm.2020.75788.

H. U. Jang, H. Y. Choi, D. Kim, J. Son, and H. K. Lee, “Fingerprint spoof detection using contrast enhancement and convolutional neural networks,” Lect. Notes Electr. Eng., vol. 424, pp. 331–338, 2017, doi: 10.1007/978-981-10-4154-9_39.

Y.-Q. Li, D.-T. Lin, and Z.-W. Yeh, “Improving Deep Learning for Face Verification Using Color Histogram Equalization Data Augmentation,” Proc. 5th World Congr. Electr. Eng. Comput. Syst. Sci., no. Mvml, pp. 1–7, 2019, doi: 10.11159/mvml19.103.

S. S. Pasha, P. S. Babu, and Z. Vakil, “Enhancement of MRI Brain Images with Histogram Equalization Techniques,” 2019 Int. Conf. Emerg. Trends Sci. Eng. ICESE 2019, 2019, doi: 10.1109/ICESE46178.2019.9194629.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Penerapan Deep Learning Menggunakan VGG-16 untuk Klasifikasi Citra Glioma


  • There are currently no refbacks.

Copyright (c) 2022 Annisa Putri, Benny Sukma Negara, Suwanto Sanjaya

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Jurnal Sistem Komputer dan Informatika (JSON)
Dikelola oleh STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email :

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.